Triple
T21740145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amanda Borden |
E536634
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Amanda Borden |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Amanda Borden | Statement: [Amanda Borden, name, Amanda Borden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amanda Borden Context triple: [Amanda Borden, name, Amanda Borden]
-
A.
Amanda Borden
chosen
Amanda Borden is an American artistic gymnast best known as the captain of the gold medal–winning U.S. women’s team at the 1996 Atlanta Olympics.
-
B.
Amanda Woodward
Amanda Woodward is a powerful, manipulative advertising executive and one of the central, iconic characters on the 1990s TV drama "Melrose Place."
-
C.
Amanda Clayton
Amanda Clayton is an American actress best known for her role in the crime drama television series "City on a Hill."
-
D.
Amanda Bowers
Amanda Bowers is a film producer known for her work on the movie "Like Father."
-
E.
Laura Bickford
Laura Bickford is an American film producer best known for her work on acclaimed independent and studio films, including the Oscar-winning drama "Traffic."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e0c46df5448190b4322127ffc4c690 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a714c208190b96efe23ed3bf0db |
completed | April 28, 2026, 2:24 a.m. |
Created at: April 16, 2026, 6:49 p.m.